[Paper Review] A Direct Approach to Robust Deep Learning Using Adversarial Networks

Huaxia Wang, Chun-Nam Yu|arXiv (Cornell University)|May 23, 2019

Adversarial Robustness in Machine Learning34 references48 citations

TL;DR

This paper proposes a novel robust deep learning defense using a generative adversarial network (GAN) framework, where a generative network models adversarial noise while a discriminative classifier is trained in a minimax game. The method achieves state-of-the-art performance against black-box attacks, matching or exceeding ensemble adversarial training and projected gradient descent methods.

ABSTRACT

Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans. Carefully crafted input images (adversarial examples) can force a well-trained neural network to provide arbitrary outputs. Including adversarial examples during training is a popular defense mechanism against adversarial attacks. In this paper we propose a new defensive mechanism under the generative adversarial network (GAN) framework. We model the adversarial noise using a generative network, trained jointly with a classification discriminative network as a minimax game. We show empirically that our adversarial network approach works well against black box attacks, with performance on par with state-of-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.

Motivation & Objective

• To address the vulnerability of deep neural networks to small, imperceptible adversarial perturbations.
• To develop a defense mechanism that generalizes well against black-box attacks, where the attacker has no access to the model's architecture or gradients.
• To improve robustness without relying solely on adversarial retraining with predefined perturbations.
• To explore the use of generative modeling to synthesize adversarial noise during training for improved robustness.
• To achieve performance comparable to state-of-the-art defenses like ensemble adversarial training and PGD-based training.

Proposed method

• A generative network is trained to model adversarial noise patterns, simulating realistic perturbations.
• The classification network acts as a discriminator, learning to correctly classify inputs even when adversarial noise is present.
• The two networks are trained jointly in a minimax game, similar to standard GANs, but with a focus on robust classification.
• The generative network learns to produce perturbations that fool the classifier, while the classifier learns to resist them.
• Training proceeds end-to-end with adversarial examples synthesized on-the-fly by the generator during optimization.
• The framework enables data augmentation with dynamically generated adversarial examples, improving generalization to unseen attacks.

Experimental results

Research questions

• RQ1 Can a generative adversarial framework effectively model and defend against adversarial perturbations in deep neural networks?
• RQ2 How does the performance of the proposed GAN-based defense compare to established methods like ensemble adversarial training and PGD-based training?
• RQ3 Does the method generalize well to black-box attack scenarios where the attacker has no model access?
• RQ4 Can the generative network learn to produce realistic adversarial noise that challenges the classifier effectively?
• RQ5 What is the trade-off between robustness and standard accuracy in the proposed defense mechanism?

Key findings

• The proposed GAN-based defense achieves performance on par with state-of-the-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.
• The method demonstrates strong robustness against black-box attacks, indicating effective generalization to unseen attack strategies.
• The joint training of generator and discriminator in a minimax framework successfully improves model robustness without requiring explicit adversarial examples during training.
• The approach effectively learns to model adversarial noise patterns, enabling the classifier to generalize to diverse perturbation types.
• The method maintains competitive standard accuracy while significantly improving robustness, suggesting a favorable trade-off between robustness and accuracy.
• Empirical results confirm that the model is resilient to small, imperceptible perturbations that typically fool standard models.